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<td class="spacerbg" width="118"><span class="default"><a href="http://www.cse.lehigh.edu/%7Ebaird/Courses/2006-07/Fall/hsbCV.pdf" class="link">Vita (PDF)</a><br>
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  <p><span class="default"> <font size="+2"><br>
    Henry S. Baird&nbsp;&nbsp;&nbsp; <small><span style="text-decoration: underline; color: rgb(153, 0, 0);">Spring
      2014
      Course</span><br>
  <br>
          </small> <br style="font-weight: bold;">
          <span style="font-style: italic;"><big><span style="font-weight: bold;">Pattern
            Recognition</span><br>
  <br>
            </big><small><big>CSE 426/326 &nbsp; <small>3 credits<br>
            <br>
              </small></big><small><small><big>CRNs:&nbsp; 18017 (426); 18016 (326)</big><br>
                </small></small>
          </small> </span></font></span><br>
    <small><small><small>&nbsp;</small></small></small><br>
          <small> <span style="color: rgb(51, 51, 255);"><span style="text-decoration: underline;">Note
            to undergraduates</span>:</span><span style="font-weight: bold; color: rgb(51, 51, 255);">&nbsp; </span><span style="color: rgb(51, 51, 255);">you
              are very welcome in this course; if you take it at the 326 level, you'll do the same programming
              exercises as the grad students, but&nbsp;shorter HWs and exams.<br>
  <br>
  </span></small><span style="color: rgb(51, 51, 255); text-decoration: underline;"><small>Note
    to CS&nbsp;M.S. graduate students:</small></span><b style="color: rgb(51, 51, 255);"><small>&nbsp; </small></b><small style="color: rgb(51, 51, 255);">this
      course fulfills both the "Theory" and "Applied Theory" skill areas.</small><small><span style="color: rgb(51, 51, 255);"><br>
  <br>
  </span></small>An
    introduction to the state of the art of pattern recognition, and the machine-learning
    theory, algorithms, and systems architectures that underlie them.<br>
  <br>
    Theoretical topics will include Bayesian decision theory,
    statistically trainable vector-space classifiers,&nbsp;parametric
    classifiers (for, <span style="font-style: italic;">e.g.,</span>likelihoods
    with Gaussian densities),
    non-parametric classifiers (<span style="font-style: italic;">e.g.</span> nearest neighbors), Perceptrons,
    generalized linear discriminants, kernel-based methods,
    decision trees, support-vector machines, neural nets, ensembles,
    and randomized classifiers. Also, we study general methodological
    issues, including best practices for statistical training
    and testing,&nbsp;the curse of dimensionality, and feature
    selection.<br>
  <br>
    The last 1/4 of the course focuses on engineering
    challenges illustrated in applications chosen&nbsp;from the computer-vision R&amp;D literature. These reflect
    state-of-the-art approaches to segmentation, contextual analysis
    (including
    syntax and semantics), autonomous adaptation, style-conscious
    recognition, and anytime algorithms. <br>
  <br>
    Weekly written homeworks or short programming exercises.&nbsp;
    A midterm exam.&nbsp;
    Students will have a choice among (1) a final exam, (2) a software project on a
    research problem from computer vision, docunment image analysis, digital libraries, or Web security,
    or (3) a literature review report on a narrow technical topic related to this course.<br>
  </p>
  <p><b><style="font-weight: bold;"><span style="color: rgb(153, 0, 0);">Course objective:</span></b></font> &nbsp;
    On completing this course, students will be sufficiently familiar
    with the theory, notation, and vocabulary of
    pattern recognition and machine learning to be able to pursue matters
    of interest in the
    current technical literature. They will also have a grasp of key
    engineering issues arising in applications. <br>
      <br>
    
      <b><style="font-weight: bold;"><span style="color: rgb(153, 0, 0);">Textbook:&nbsp;</span></b><i>Pattern
            Classification (2<sup>nd</sup> Ed.),</i> R. O. Duda,
    P. E. Hart, &amp;
    D. G. Stork, John Wiley &amp; Sons, October 2000. 680 pages. ISBN
    0-471-05669-3. &nbsp;If there are no copies available in the Univ. bookstore, email me immediately.  (There is also available, from the same publisher, a companion <em>Computer Manual in MATLAB</em> for this textbook; if you want to use MATLAB for the programming exercises in this course, I urge you buy this manual also---but it is <strong>not</strong> required.)<br>
      <br>
    
    
      <b><style="font-weight: bold;"><span style="color: rgb(153, 0, 0);">Lectures Time and Place:</span></b>&nbsp;Tues/Thurs 9:20 AM - 10:35 AM,   Packard Laboratory room 258.<span style="font-style: italic;"></span>
      <br>
      <br>
    
      <big style="font-weight: bold;"><font size="3"><font color="#800000"><span style="color: rgb(153, 0, 0);"> Instructor: &nbsp;</span>
      </font></font></big> Prof.
    Henry Baird, <a href="mailto:hsb2@lehigh.edu" class="style1">hsb2@lehigh.edu</a>. Office:&nbsp; Packard Lab 380.
    &nbsp;
    Office Hours: &nbsp;Wednesdays 12:10-1:00 PM or by appointment. He will grade all exams and final project reports.</p>
  <p><big style="color: rgb(153, 0, 0); font-weight: bold;">Grader (for homeworks only):</big></p>
  <p>I am seeking a grader for this course, which will pay at least $1200, for up to 10 hours/week work, for the full 12 weeks of the semester. Te grader willl handle homeworks only (the Instructir will grade all exams and project reports). I provide detailed written solution sets for every assignment. Homework assignments are (about) half programming exercises, and the rest are mathematics and other theoretical problems. The programming exercises need to be graded only for their <em>results</em>, not for the quality of their code. During some weeks, not all students will submit homeworks (since they'll be working on multy-week projects).</p>
  <p>NOTE to studengs: Once we have a grader, to ask questions about homework problems or grades, contact him/her by email; if you wish, he/she will arrange to meet you in person.<br>
    <br>
    <span style="font-weight: bold; color: rgb(153, 0, 0);"> Course Site:&nbsp;</span> <span style="font-weight: bold;">CSE-426-CSE-326-SP14 Pattern Recognition</span>.
    &nbsp;We will use the University's online CourseSite facility to
    distribute lecture slides, homework assignments, experimental data sets, and grades.
    &nbsp;As soon as you&nbsp;are enrolled in this course, please browse <a href="http://coursesite.lehigh.edu"><span style="font-family: monospace;">coursesite.lehigh.edu</span></a> &nbsp;and try to login: &nbsp;if you cannot, send the instructor email. <br>
    <br>
    
    <big style="font-weight: bold;"><font size="3"><font color="#800000"><span style="color: rgb(153, 0, 0);">
      Prerequisites (if you have any questions about these, ask the Instructor):</span><br>
      <br>
      </font></font></big> CSE/Math 340:&nbsp; <i>Design and Analysis of Algorithms -- </i>or
    comparable background in basic algorithms and data structures.<i><br>
      </i> <br>
    Math 205: <i>Linear Algebra</i> etc -- or similar familiarity with linear algebra &amp; matrices.<br>
    <br>
    Math 231 or Math 309 or CSC
    450: <i>Applied Probability &amp; Statistics</i> -- or
    some background
    in discrete probability
    and applied statistics &amp; data analysis.<br>
    <br>
    CSE 109: <i>Programming
      in C++
      -- </i><span style="font-style: normal;">or enough
        experience with
        C++, Java, C, or (ideally) MatLab</span> to complete a small software
    project
    without faculty supervision. </p>
  <p><b><span style="font-style: italic;">If you
        have any questions, ask the instructor: <a href="mailto:hsb2@lehigh.edu" class="style1">hsb2@lehigh.edu</a>.</span></b><br>
        </p>
  <p><b style="color: rgb(153, 0, 0);">Accommodations for Students
    with Disabilities</b><span style="color: rgb(153, 0, 0);">:</span>&nbsp;
    If you have a disability for which you are or may be requesting
    accommodations, please contact both your instructor and the Office of
    Academic Support Services, University Center C212 (610-758-4152) as
    early as possible in the semester.&nbsp; You must have
    documentation from
    the Academic Support Services office before accommodations can be
    granted.<br>
  </p>
  <p><span class="graybg" style="text-align: left;">Lehigh University endorses The Principles of Our Equitable Community (<a href="http://www4.lehigh.edu/diversity/principles">http://www4.lehigh.edu/diversity/principles</a>). We expect each member of this class to acknowledge and practice these Principles. Respect for each other and for differing viewpoints is a vital component of the learning environment inside and outside the classroom. </span><br>    </p>
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